Ross Wightman
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4 years ago | |
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| README.md | ||
| results-imagenet-a.csv | 4 years ago | |
| results-imagenet.csv | 4 years ago | |
| results-imagenetv2-matched-frequency.csv | 4 years ago | |
| results-sketch.csv | 4 years ago | |
README.md
Validation Results
This folder contains validation results for the models in this collection having pretrained weights. Since the focus for this repository is currently ImageNet-1k classification, all of the results are based on datasets compatible with ImageNet-1k classes.
Datasets
There are currently results for the ImageNet validation set and 3 additional test sets.
ImageNet Validation - results-imagenet.csv
- Source: http://image-net.org/challenges/LSVRC/2012/index
- Paper: "ImageNet Large Scale Visual Recognition Challenge" - https://arxiv.org/abs/1409.0575
The standard 50,000 image ImageNet-1k validation set. Model selection during training utilizes this validation set, so it is not a true test set. Question: Does anyone have the official ImageNet-1k test set classification labels now that challenges are done?
ImageNetV2 Matched Frequency - results-imagenetv2-matched-frequency.csv
- Source: https://github.com/modestyachts/ImageNetV2
- Paper: "Do ImageNet Classifiers Generalize to ImageNet?" - https://arxiv.org/abs/1902.10811
An ImageNet test set of 10,000 images sampled from new images roughly 10 years after the original. Care was taken to replicate the original ImageNet curation/sampling process.
ImageNet-Sketch - results-sketch.csv
- Source: https://github.com/HaohanWang/ImageNet-Sketch
- Paper: "Learning Robust Global Representations by Penalizing Local Predictive Power" - https://arxiv.org/abs/1905.13549
50,000 non photographic (or photos of such) images (sketches, doodles, mostly monochromatic) covering all 1000 ImageNet classes.
ImageNet-Adversarial - results-imagenet-a.csv
- Source: https://github.com/hendrycks/natural-adv-examples
- Paper: "Natural Adversarial Examples" - https://arxiv.org/abs/1907.07174
A collection of 7500 images covering 200 of the 1000 ImageNet classes. Images are naturally occuring adversarial examples that confuse typical ImageNet classifiers. This is a challenging dataset, your typical ResNet-50 will score 0% top-1.
TODO
- Add rank difference, and top-1/top-5 difference from ImageNet-1k validation for the 3 additional test sets
- Explore adding a reduced version of ImageNet-C (Corruptions) and ImageNet-P (Perturbations) from https://github.com/hendrycks/robustness. The originals are huge and image size specific.